Joint theme and event based rating model for identifying relevant influencers on Twitter: COVID-19 case study

Srour, Ali; Ould-Slimane, Hakima; Mourad, Azzam; Harmanani, Haidar M.; Jenainati, Cathia

doi:10.1016/j.osnem.2022.100226

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…Other studies have focused on analyzing the misinformation spread on Twitter and other social media platforms to exacerbate negative emotions [25,26]. Furthermore, the COVID-19 topic has been employed as a case study for developing novel analysis models, such as identifying relevant influencers [27] and detecting emotions [28], among others.…”

Section: Related Workmentioning

confidence: 99%

“…As expected, among the accounts that have posted pro-conflict hashtags, both approaches associated the highest rate of pro-conflict tweets to real accounts, even though the presence of about 33% of tweets associated with non-real accounts reveals that information on social networks is often disrupted by fake accounts. war 27 .…”

Section: Rq4: Is It Possible To Detect Bots Among Twitter Accounts Th...mentioning

confidence: 99%

“…In fact, while bots, in particular the ones classified as Astroturf and Fake follower, produce few tweets in the first few days, on the 2nd and 3rd of March, they shared a conspicuously higher number of tweets. This might be due to the creation of several bots during those days for the purpose of amplifying disinformation and immediately sharing pro-conflict tweets (See footnote 27,28).…”

Section: Rq4: Is It Possible To Detect Bots Among Twitter Accounts Th...mentioning

confidence: 99%

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Analyzing the worldwide perception of the Russia-Ukraine conflict through Twitter

Breve,

Caruccio,

Cirillo

et al. 2024

J Big Data

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In this paper, we analyze the worldwide perception of the Russia-Ukraine conflict (RU conflict for short) on the Twitter platform. The study involved collecting over 17 million tweets written in 63 different languages and conducting a multi-language sentiment analysis, as well as an analysis of their geographical distribution and verification of their temporal relationship to daily events. Additionally, the study focused on analyzing the accounts producing pro-conflict tweets to evaluate the possible presence of bots. The results of the analysis showed that the war had a significant global impact on Twitter, with the volume of tweets increasing as the war’s threats materialized. There was a strong correlation between the succession of events, the volume of tweets, and the prevalence of a specific sentiment. Most tweets had a negative sentiment, while tweets with positive sentiment mainly contained support and hope for people directly involved in the conflict. Moreover, a bot detection analysis performed on the collected tweets revealed the presence of many accounts spreading tweets including pro-conflict hashtags that cannot be identified as real users. Overall, this study sheds light on the importance of social media in shaping public opinion during conflicts and highlights the need for reliable methods to detect bots.

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Section: Related Workmentioning

confidence: 99%

Section: Rq4: Is It Possible To Detect Bots Among Twitter Accounts Th...mentioning

confidence: 99%

Section: Rq4: Is It Possible To Detect Bots Among Twitter Accounts Th...mentioning

confidence: 99%

See 1 more Smart Citation

Analyzing the worldwide perception of the Russia-Ukraine conflict through Twitter

Breve,

Caruccio,

Cirillo

et al. 2024

J Big Data

View full text Add to dashboard Cite

show abstract

“…Societies and economies should take note of the COVID-19 virus as a warning, since both were caught off guard and now face serious consequences ( Lopes and Reis, 2021 , Chopra et al, 2022 , Srour et al, 2022 , Narayan et al, 2022 , Alowibdi et al, 2021 ). Globally, the current pandemic has had severe health, social, political, and economic consequences.…”

Section: Introductionmentioning

confidence: 99%